I am Sang Choe, a third-year CS PhD student in
Language Technologies Institute at
Carnegie Mellon University, advised by
Eric Xing.
My research aims to make AI programming easier for all developers, ranging
from academic researchers to industry practitioners. Towards this goal, I work on
designing a high-level AI programming framework that abstracts away low-level programming
details and improves automation and reusability, while minimizing abstraction penalty
such as flexibilty and efficiency. Observing that the advancement in programming frameworks
in traditional software technologies (e.g. TypeScript, Django) has significantly accelerated
their adoption, I believe we will see the similar trend in
AI soon, hopefully led by my research!
In technical terms, my research lies in the intersection of:
◆ programming models for machine learning
◆ automated machine learning
◆ data-centric AI
Previously, I completed MS in Language Technologies at Carnegie Mellon University under the guidance of Jaime Carbonell. Before that, I earned BS in Electrical Computer Engineering & Mathematics (double major) from Seoul National University. I had also spent time as a research intern at Microsoft in 2021.
Betty: An Automatic Differentiation Library for Multilevel Optimization
[code]
ICLR, 2023
Sang Keun Choe, Willie Neiswanger, Pengtao Xie, and Eric Xing
Oral (1.8% acceptance rate)
Pollux: Co-adaptive Cluster Scheduling for Goodput-Optimized Deep Learning
[code]
OSDI, 2021
Aurick Qiao, Sang Keun Choe, Suhas Subramanya, Willie Neiswanger, Qirong Ho, Hao Zhang, Greg Ganger, Eric Xing
🏆 Jay Lepreau Best Paper Award
On Orthogonal Jacobian Regularization in Deep Neural Networks
SEDL Workshop @ NeurIPS, 2019
Sang Keun Choe*, Hosan Jeong*, Jaime Carbonell
On Leveraging the Visual Modality for Neural Machine Translation
INLG, 2019
Vikas Raunak*, Sang Keun Choe*, Quanyang Lu*, Yi Xu*, Florian Metze
Audio Cover Song Identification using Convolutional Neural Network ICASSP, 2017
Sungkyung Chang, Juheon Lee, Sang Keun Choe, Kyogu Lee